Next Article in Journal
DIRT: The Dacus Image Recognition Toolkit
Previous Article in Journal
Green Stability Assumption: Unsupervised Learning for Statistics-Based Illumination Estimation
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
J. Imaging 2018, 4(11), 128; https://doi.org/10.3390/jimaging4110128

Improving Tomographic Reconstruction from Limited Data Using Mixed-Scale Dense Convolutional Neural Networks

1
Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands
2
Department of Mathematics, University of California, Berkeley, CA 94720, USA
3
Center for Applied Mathematics for Energy Research Applications, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
*
Author to whom correspondence should be addressed.
Received: 1 September 2018 / Revised: 25 September 2018 / Accepted: 10 October 2018 / Published: 30 October 2018
Full-Text   |   PDF [49504 KB, uploaded 30 October 2018]   |  

Abstract

In many applications of tomography, the acquired data are limited in one or more ways due to unavoidable experimental constraints. In such cases, popular direct reconstruction algorithms tend to produce inaccurate images, and more accurate iterative algorithms often have prohibitively high computational costs. Using machine learning to improve the image quality of direct algorithms is a recently proposed alternative, for which promising results have been shown. However, previous attempts have focused on using encoder–decoder networks, which have several disadvantages when applied to large tomographic images, preventing wide application in practice. Here, we propose the use of the Mixed-Scale Dense convolutional neural network architecture, which was specifically designed to avoid these disadvantages, to improve tomographic reconstruction from limited data. Results are shown for various types of data limitations and object types, for both simulated data and large-scale real-world experimental data. The results are compared with popular tomographic reconstruction algorithms and machine learning algorithms, showing that Mixed-Scale Dense networks are able to significantly improve reconstruction quality even with severely limited data, and produce more accurate results than existing algorithms. View Full-Text
Keywords: machine learning; deep learning; image reconstruction; tomography machine learning; deep learning; image reconstruction; tomography
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Pelt, D.M.; Batenburg, K.J.; Sethian, J.A. Improving Tomographic Reconstruction from Limited Data Using Mixed-Scale Dense Convolutional Neural Networks. J. Imaging 2018, 4, 128.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
J. Imaging EISSN 2313-433X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top